Starguide - A Practical Guide to Astro-Photography

Can be downloaded from: Narrowband Normalization – Google Drive

Installation and usage remark from the development team:

Note:  You might run into an issue with installing scripts in the latest versions of Pixinsight where the script or processing icons don't show up in the menu after installation.  To fix this, go to: Resources-Updates-Reset Update. 

We are proud to release the Narrowband Normalization (NBN) Process for Pixinsight Software.  Mike Cranfield and I decided to take things a step further by moving away from pixelmath scripts and provide you with an actual Pixinsight Process with graphical interface so you can visualize the Normalization Process in real-time.    

The normalization process is an automated curve stretch we perform using math on the OIII and/or SII data channels to boost the intensity of these channels to that of the HA channel. This boost attempt will try to equalize the data to help better show off the gases located within the nebula.

The NBN process works on both linear and non-linear data (starless); however, there is benefit to NBN processing on non-linear data as we have introduced a Lightness feature which will allow you to use different lightness modes to help show off your image from many different perspectives, i.e. you can select Ha, or SII for example. Lightness mode does not work on linear data!   

We also offer multiple Pallet modes depending on the data you are trying to process; for example, if you have an image from OSC camera which was taken with a dual narrowband filter, you will want to select the HOO pallet.

If you are processing mono data and have an SHO, HOO, HSO image, you need to select the appropriate pallet for that data, i.e., SHO for SHO data. 

These pallet modes DO NOT create an SHO image from HOO data, so make sure you have your data compiled before running NBN and run the appreciate NBN pallet.  NBN will make an attempt to equalize these channels while maintaining background neutral, but we do give you other options to increase/decrease the channel boost so you have more control of the image.   

Example: HOSNormalization_V8 script:

/*       Normalize HOS data with Pixelmath - V8

          by Bill Blanshan and Mike Cranfield                */

                    

Mode=           1 ; //<-- Data type, 0: linear, 1: Non-linear

Lightness=      1 ; //<-- 0=OFF, 1=Original, 2=Ha, 3=SII, 4=OIII

SCNR=           1 ; //<-- 0=OFF, 1=On

Blackpoint=  1.00 ; //<-- Blackpoint range (0 to 1= min to med)

SIIBoost=    1.00 ; //<-- Increase to boost SII

OIIIBoost=   1.00 ; //<-- Increase to boost OIII

HLRecover=   1.00 ; //<-- If highlights are clipped, increase >1

HLReduction= 1.00 ; //<-- If highlights are too bright, increase >1

Brightness=  1.00 ; //<-- Increase to stretch image

/* Drag the bottom left arrow over to your HOS image

 Hope you enjoy!!!

Important Operation notes:

The preferred choice for narrowband normalization is on a

non-linear starless image.  You can run this process on a

linear image, however you will not be able to use the new

Lightness feature.  When stretching an image that has been

normalized in linear mode, the preferred stretch method after

is a "Linked" stretched

V7 update Notes and Instructions:

Notes:  In this release, we simplified the normalization

process plus giving you the option to normalize linear

and non-linear images.  It is still recommended this process

be performed on a starless image as the normalization

process can hurt the star color.  Also, we are converting

the image from RGB to the LAB color space which offers

better lighting control.  By using the "Lightness" of

the original image or the Lightness from the Ha data,

helps maintain color and brightness control far better

than when using luminance as done in Version 5. The

downside is,the Lightness feature only works on

non-linear images. 

1) Mode:  When processing linear data (unstretched),

   select mode:0. When processing none-linear data,

   select mode:1.  The advantage of processing in

   linear state is you can stretch the data afterwards

   using a linked or un-linked stretch to give you

   different appearances. The downside is, you cannot

   use the Lightness feature as it only works on

   non-linear images (Mode:1).

      

2) Lightness:  The advantage of using Lightness is to

   help control color and brightness better within the

   LAB color space.  It is also good for showing off

   more detail i.e. if Ha "2" mode is used, you will see

   detail within the Ha regions of the image. When Lightness

   feature is set to Off "0", the produced image has

   no Lightness correction. When set to "1", the produced

   image using the original image Lightness. This feature

   is only available for Non-Linear images.

3) SCNR:  When On, any green cast will be removed while

   retaining its luminance 

4) Blackpoint:  This feature allows you to control where

   normalization turn on in the blackpoint zone. This is

   an interpolation between the minimum and median image

   values, so a Blackpoint of 0 will begin the normalization

   after the minimum image value.  A value of 1 will do the

   same but starts the normalization after the median image

   value. Any value can be used between 0 and 1 e.g. 0.85.

   This feature helps allow fainter signals to be normalized

   but subsequently can allow for more noise.  

5) OIII & SIIBoost:  These feature allows you to boost or

   reduce more OIII/SII signal into your image. a value

   greater than 1 will boost and a value less than 1 will

   reduce. It is unlikely you will need to use this but

   I gave you the option just in case.

6) HLRecover:   This features is a linear rescaling method

   to help recover highlights in case they have been clipped.

   The default value is 1, so if you run this normalization

   process and your image is clipped, you can increase this

   value to unclip the image e.g. a value of 1.1 or 1.2 might

   be ok. Always leave at value 1 unless you need to recover

   clipped data.

8) HLReduction:  This feature is a reversed curve stretch

   on the highlights alone. example, the Rosette nebula has

   a bright core.  You can use this method by increasing the

   value to greater than 1 to help reduce the highlights while

   keeping your shadows the same.  This method can also work

   in reverse, meaning, you can reduce this value to increase

   highlights ;-)

9) Brightness:  This is the final step within this

   pixelmath script.  This feature will perform a curve

   stretch to increase overall brightness of the image with

   a value greater than 1. A value less than 1 will reduce

   brightness but should not be used to regain clipping, that

   should be done using the HLRecover feature.

Notes:

   

1) It is suggested to run normalization on a starless

   image. An image with stars will can result in severely

   discolored stars after normalization.

   

2) Regarding stretching of normalized "linear" data, it is

   recommended to use a linked stretch, such as GHS,

   EZ Soft Stretch, or my pixelmath stretch scripts.

   If you plan on performing normalization on an image in

   a non-linear state and are using my pixelmath scripts,

   it is best to use the unlinked stretch as this will

   also help with boosting OIII/SII signals. Otherwise,

   please use GHS, EZ Soft Stretch or my pixelmath script.

   Also, don't over stretch your image!  This can happen

   when using stretching scripts/maths if the image has

   been denoised in the linear stage. So please control

   your stretching :-)

3) Make sure your starless image is background neutralized!

   When performing this on a non-linear image, and using

   the BackgroundNeutralization process, it is best to set

   the BackgroundNeutralization Upper limit value to a

   value higher than your background reading.  Then set the

   working mode to "Target Background" and use a value

   to your liking e.g. 0.150.  This helps give good results

   for normalization. 

4) Preferred method for adding the stars back is using

   our "Screen Stars" script, or my RGB to Narrowband

   pixelmath so that star color is preserved.    

*/

M= (min($T)+Blackpoint*(med($T)-min($T))/1);

E0= adev($T)/1.2533 + mean($T) - M;

A0= E0/~M[1];

E1= (A0[1]*(1-A0[0])/(A0[1] - 2*A0[1]*A0[0] + A0[0]))/OIIIBoost;

E2= rescale($T[1], M[1],1);

E3 = ~(~mtf(E1, E2) * ~min($T[1], M[1]));

A1= E0/~M[2];

E4= (A0[2]*(1-A0[0])/(A0[2] - 2*A0[2]*A0[0] + A0[0]))/SIIBoost;

E5= rescale($T[2], M[2],1);

E6 = ~(~mtf(E1, E5) * ~min($T[2], M[2]));

R= $T[0];

G= iif(SCNR==0,E3,min(mean($T[0],E6),E3));

B= E6;

R1= iif(R>0.04045, ((R+0.055)/1.055)^ 2.4,R/ 12.92);

G1= iif(G>0.04045, ((G+0.055)/1.055)^ 2.4,G/ 12.92);

B1= iif(B>0.04045, ((B+0.055)/1.055)^ 2.4,B/ 12.92);

X= ((R1*0.4360747) + (G1*0.3850649) + (B1*0.1430804));

Y= ((R1*0.2225045) + (G1*0.7168786) + (B1*0.0606169));

Z= ((R1*0.0139322) + (G1*0.0971045) + (B1*0.7141733));

X1= iif(X>0.008856, X^(1/3),(7.787*X)+(16/116) );

Y1= iif(Y>0.008856, Y^(1/3),(7.787*Y)+(16/116) );

Z1= iif(Z>0.008856, Z^(1/3),(7.787*Z)+(16/116) );

L= 116*Y1-16;  

a= (500*(X1-Y1));  

b= (200*(Y1-Z1));   

Y2= iswitch(Lightness==0,(L+16)/116,

            Lightness==1,(CIEL($T)+.16)/1.16,

            Lightness==2,($T[0]+.16)/1.16,

            Lightness==3,($T[2]+.16)/1.16, ($T[1]+.16)/1.16);

X2= (a/500)+Y2;

Z2= Y2-(b/200);

X3= iif(X2>0.008856, X2^3,(X2-16/116)/7.787);

Y3= iif(Y2>0.008856, Y2^3,(Y2-16/116)/7.787);

Z3= iif(Z2>0.008856, Z2^3,(Z2-16/116)/7.787);

R2= ((X3* 3.1338561) + (Y3*-1.6168667) + (Z3*-0.4906146));

G2= ((X3*-0.9787684) + (Y3* 1.9161415) + (Z3* 0.0334540));

B2= ((X3* 0.0719453) + (Y3*-0.2289914) + (Z3* 1.4052427));

R3= iif(R2>0.0031308, 1.055*(R2^(1/2.4))-0.055,12.92*R2);

G3= iif(G2>0.0031308, 1.055*(G2^(1/2.4))-0.055,12.92*G2);

B3= iif(B2>0.0031308, 1.055*(B2^(1/2.4))-0.055,12.92*B2);

E10= iif(Mode==0,iif($T==$T[0],R,iif($T==$T[1],G,B)),

     iif($T==$T[0],R3,iif($T==$T[1],G3,B3)) );

E11= (mtf(~(1/HLReduction*.5),E10)*E10)+(E10*~E10);

E12= mtf((1/Brightness*.5),E11);

E13= rescale(E12,0,HLRecover);

E13

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